Occupational Therapist Perspectives: Factors Influencing Recovery Following Motor Vehicle Accident Injury
Bibliographic record
Abstract
Background. Motor vehicle accident (MVA) injuries can result in persistent impairments which contribute to chronic pain, mental health symptoms, and decreased quality of life. Occupational therapists play a key role in the rehabilitation of those injured in MVAs yet there is lack of evidence to inform occupational therapy practice. An explicit understanding of the factors influencing post-MVA recovery from occupational therapists’ perspectives is needed to inform clinical service delivery. Purpose. This study addressed the following question: From the perspective of occupational therapists, what factors are identified as influencing recovery following a noncatastrophic injury sustained in an MVA? Method. An interpretive descriptive study design was used. Data were collected through semistructured interviews with 10 occupational therapists who provide auto-insurer funded occupational therapy to clients with noncatastrophic injuries from an MVA. Data were analyzed using constant comparative analysis. Results. Physical symptoms and accessibility, acceptance, social support, access to occupational therapy, and navigating the insurance system were factors identified as influencing post-MVA recovery. Conclusion. This study highlights the importance of using a biopsychosocial lens when working with clients post-MVA. Recovery post-MVA needs to be considered in the context of the insurance system, as navigating the insurance system was a predominant factor influencing recovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".